Skip to Main content Skip to Navigation
Journal articles

Robust Iris Segmentation on Uncalibrated Noisy Images Using Mathematical Morphology

Miguel Ángel Luengo-Oroz 1, 2 Emmanuel Faure 2, 3 Jesus Angulo 4
1 CIBER-BBN - Biomedical Image Technologies, Biomedical Research Center
2 ISC-PIF - Institut des Systèmes Complexes - Paris Ile-de-France
3 CREA - Centre de recherche en épistémologie appliquée
4 CMM - Centre de Morphologie Mathématique
Abstract : This paper proposes a new approach for fast iris segmentation that relies on the closed nested structures of iris anatomy (the sclera is brighter than the iris, and the iris is brighter than the pupil) and on its polar symmetry. The described method applies mathematical morphology for polar/radial-invariant image filtering and for circular segmentation using shortest paths from generalized grey-level distances. The proposed algorithm obtained good results on the NICE-I contest and showed a very robust behavior, especially when dealing with half-closed eyes, different skin colours/illumination or subjects wearing glasses. © 2009 Elsevier B.V. All rights reserved.
Keywords : Grey-level generalized distance Iris segmentation Non-cooperative iris biometry Spatially variant mathematical morphology
Document type :
Journal articles
Complete list of metadata
https://hal-mines-paristech.archives-ouvertes.fr/hal-00836060
Contributor : Sylvie Lavigne <>
Submitted on : Thursday, June 20, 2013 - 1:38:27 PM
Last modification on : Monday, February 15, 2021 - 10:51:36 AM

Identifiers

Collections

CNRS | ENS-CACHAN | ENSMP_CMM | ENSMP | UPMC | CURIE | FNCLCC | X-CREA | X | PARISTECH | UNIV-PARIS1 | INSTITUT-TELECOM | SORBONNE-UNIVERSITE | SU-SCIENCES | GDMM | PSL | ENS-PARIS-SACLAY

Citation

Miguel Ángel Luengo-Oroz, Emmanuel Faure, Jesus Angulo. Robust Iris Segmentation on Uncalibrated Noisy Images Using Mathematical Morphology. Image and Vision Computing, Elsevier, 2010, 28 (2), pp.278-284. ⟨10.1016/j.imavis.2009.04.018⟩. ⟨hal-00836060⟩

Export

BibTeX TEI DC DCterms EndNote

Share

Metrics

Record views

948